Skip to content

7 Publications in 2018

At EURA NOVA, we believe investing in research allows us to continuously become more proficient, to maintain our know-how at the cutting edge of IT, and to share its benefits with our customers. As we look back on the year 2018, we are both proud and happy to announce that our R&D department has published 7 publications this year:


Firstly, our paper “Pairwise Image Ranking with Deep Comparative Network” was published at the 26th European Symposium on Artificial Neural Networks. The paper, written by our Lead R&D engineer Aymen Cherif and Salim Jouili, discuss how using the pair-wise ranking model can provide better results for instance-level image retrieval.

Aymen Cherif, Salim Jouili, Pairwise Image Ranking with Deep Comparative Network. ESANN 2018: ES2018-200


Secondly, our R&D engineer Cécile Pereira participated in the redaction of a paper published in Bioinformatics in May 2018. They propose a novel end-to-end deep learning approach for biomedical NER tasks that leverage the local contexts based on n-gram character and word embeddings via Convolutional Neural Network.

Qile Zhu, Xiaolin Li,  Ana Conesa, Cécile Pereira, GRAM-CNN: A deep learning approach with local context for named entity recognition in biomedical text, Bioinformatics – May 2018


In July, our R&D engineer Katherine Krasnoschok was in Melbourne, Australia to attend the ACL conference. She presented her poster on topic modelling. Her paper, co-written with Salim Jouili, indicates that involving more named entities positively influences the overall quality of topics.

Katsiaryna Krasnashchok, Salim Jouili, Improving Topic Quality by Promoting Named Entities in Topic Modeling, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Vol. 2. 2018


Moreover, our paper “Graph BI & Analytics: Current State and Future Challenges” was accepted for publication and presented at the 20th International Conference on Big Data Analytics and Knowledge Discovery, taking place in Germany in September. The paper presents the state of the art of graph BI & analytics, with a focus on graph warehousing.

Amine Ghrab, Oscar Romero, Salim Jouili, Sabri Skhiri, Graph BI & Analytics: Current State and Future Challenges. DaWaK 2018: 3-18


In September as well, our paper Data Mining and Machine Learning Techniques supporting Time-based Separation Concept Deployment, co-written with Eurocontrol and WaPT, was presented at the 37th Digital Avionics Systems Conference (DASC) in London, U.K. The paper presents two methods to allow air traffic controllers to deliver separation minima accurately and safely, on the basis of time intervals instead of distances.

De Visscher, I.; Stempfel, G.; Rooseleer, F. & Treve, V.; Data mining and Machine Learning techniques supporting Time-Based Separation concept deployment, in 37th Digital Avionics Systems Conference (DASC), pp 594-603, London, UK, September 23-27, 2018


Finally, our engineer Katsiaryna Krasnashchok presented in October her poster on Hierarchical Attention-Based Neural Topic Model at the 6th International Conference on Statistical Language and Speech Processing. Furthermore, our Lead R&D engineer Aymen Cherif and our bootcamper Luca De Petris presented as well their poster on LSTM Siamese Network.

Katsiaryna Krasnashchok, Salim Jouili, Hierarchical Attention-Based Neural Topic Model, SLSP 2018

Luca De Petris, Aymen Cherif,  LSTM Siamese Network for Question Answering System, SLSP 2018

Releated Posts

Kafka Summit 2024: Announcements & Trends

The Kafka Summit brought together industry experts, developers, and enthusiasts to discuss the latest advancements and practical applications of event streaming and microservices. In this article, our CTO Sabri Skhiri
Read More

Privacy Enhancing Technologies 2024: A Summary

For Large Language Models (LLMs), Azure confidential computing offers TEEs to protect data integrity throughout various stages of the LLM lifecycle, including prompts, fine-tuning, and inference. This ensures that all
Read More